Given the emergence of data science and machine learning throughout all aspects of
society, but particularly in the scientific domain, there is increased importance placed on …

Advances in machine learning have impacted myriad areas of materials science, such as
the discovery of novel materials and the improvement of molecular simulations, with likely …

A bottleneck in efficiently connecting new materials discoveries to established literature has
arisen due to an increase in publications. This problem may be addressed by using named …

A large amount of materials science knowledge is generated and stored as text published in
peer-reviewed scientific literature. While recent developments in natural language …

Simple yet effective data augmentation techniques have been proposed for sentence-level
and sentence-pair natural language processing tasks. Inspired by these efforts, we design …

Pretrained language models (PTLMs) are typically learned over a large, static corpus and
further fine-tuned for various downstream tasks. However, when deployed in the real world …

Research publications are the major repository of scientific knowledge. However, their
unstructured and highly heterogenous format creates a significant obstacle to large-scale …

Experimental procedures for chemical synthesis are commonly reported in prose in patents
or in the scientific literature. The extraction of the details necessary to reproduce and …

The ever-increasing number of materials science articles makes it hard to infer chemistry-
structure-property relations from literature. We used natural language processing methods to …

Synthesis protocol exploration is paramount in catalyst discovery, yet keeping pace with
rapid literature advances is increasingly time intensive. Automated synthesis protocol …